# Load required libraries
library(tidyverse)
library(maps)
library(ggplot2)
library(data.table)
library(gridExtra)
library(raster)
library(sf)
library(viridis)
library(scales)
library(dplyr)
library(randomForest)
library(caret)
library(pdp)
library(knitr)
library(kableExtra)
library(future)Data from Kroodsma et al. (2018) Science, accessible at: Global Fishing Watch Data Download Portal.
The data is accessible up to the end of the year 2020, we used four entire years of data (2017, 2018, 2019, 2020) to match SAR data records.
# Set the path to the 2017-2020 folder
#path <- "Data/AIS Fishing Effort 2017-2020"
# List all CSV files in the folder
#AIS_csv_files <- list.files(path = here::here(path), pattern = "*.csv", full.names = TRUE, recursive = TRUE)
# Read all CSV files and combine them into a single data frame
#AIS_fishing <- AIS_csv_files %>%
# map_df(~read_csv(.))
load(here::here("Data","AIS_fishing.Rdata"))
# Aggregate fishing hours by latitude and longitude
aggregated_AIS_fishing <- AIS_fishing %>%
group_by(cell_ll_lat, cell_ll_lon) %>%
summarise(total_fishing_hours = sum(fishing_hours, na.rm = TRUE)) %>%
ungroup() %>%
# Remove any cells with zero or negative fishing hours
filter(total_fishing_hours > 0)
# Function to standardize coordinates to 0.1 degree resolution
standardize_coords <- function(lon, lat) {
list(
lon_std = floor(lon * 10) / 10,
lat_std = floor(lat * 10) / 10
)
}
# Standardize and aggregate AIS data
AIS_data_std <- aggregated_AIS_fishing %>%
mutate(coords = map2(cell_ll_lon, cell_ll_lat, standardize_coords)) %>%
mutate(
lon_std = map_dbl(coords, ~ .x$lon_std),
lat_std = map_dbl(coords, ~ .x$lat_std)
) %>%
group_by(lon_std, lat_std) %>%
summarise(total_fishing_hours = sum(total_fishing_hours, na.rm = TRUE), .groups = "drop")
# Create the world map
world_map <- map_data("world")
# Create the plot
ggplot() +
geom_map(data = world_map, map = world_map,
aes(long = long, lat = lat, map_id = region),
color = "black", fill = "lightgray", size = 0.1) +
geom_tile(data = AIS_data_std,
aes(x = lon_std, y = lat_std, fill = total_fishing_hours)) +
scale_fill_viridis(
option = "inferno",
direction = -1,
trans = "log1p",
name = "AIS Fishing Effort (hours; 2017-2020)",
breaks = c(0, 1, 10, 100, 1000, 10000, 100000),
labels = scales::comma,
guide = guide_colorbar(barwidth = 20, barheight = 0.5, title.position = "top", title.hjust = 0.5)
) +
coord_fixed(1.3) +
theme_minimal() +
labs(title = NULL,
x = "Longitude", y = "Latitude") +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = ggplot2::margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = ggplot2::margin(b = 10))
)
Data from Paolo et al. 2024 Nature, accessible at: Global Fishing Watch SAR Vessel Detections
The data is accessible from 2017 to today, we used four entire years of data (2017, 2018, 2019, 2020) to match AIS records.
# Set the path to the 2016 folder
#path <- "Data/SAR Vessel detections 2017-2020"
# List all CSV files in the folder
#SAR_csv_files <- list.files(path = here::here(path), pattern = "*.csv", full.names = TRUE)
# Read all CSV files and combine them into a single data frame
#SAR_fishing <- SAR_csv_files %>%
# map_df(~read_csv(.))
load(here::here("Data","SAR_fishing.Rdata"))
# Aggregate fishing hours by latitude and longitude
aggregated_SAR_fishing <- SAR_fishing %>%
mutate(
lat_rounded = round(lat, digits = 2),
lon_rounded = round(lon, digits = 2)
) %>%
group_by(lat_rounded, lon_rounded) %>%
filter(fishing_score >= 0.9) %>%
summarise(
total_presence_score = sum(presence_score, na.rm = TRUE),
avg_fishing_score = mean(fishing_score, na.rm = TRUE),
count = n()
) %>%
mutate(total_presence_score = round(total_presence_score, digits = 0)) %>%
ungroup()
# Standardize and aggregate SAR data
SAR_data_std <- aggregated_SAR_fishing %>%
mutate(coords = map2(lon_rounded, lat_rounded, standardize_coords)) %>%
mutate(
lon_std = map_dbl(coords, ~ .x$lon_std),
lat_std = map_dbl(coords, ~ .x$lat_std)
) %>%
group_by(lon_std, lat_std) %>%
summarise(total_presence_score = sum(total_presence_score, na.rm = TRUE), .groups = "drop")
# Create the world map
world_map <- map_data("world")
# Create the plot
ggplot() +
geom_map(data = world_map, map = world_map,
aes(long = long, lat = lat, map_id = region),
color = "black", fill = "lightgray", size = 0.1) +
geom_tile(data = SAR_data_std,
aes(x = lon_std, y = lat_std, fill = total_presence_score)) +
scale_fill_viridis(
option = "inferno",
direction = -1,
trans = "log1p",
name = "Fishing vessel detections (2017-2020)",
breaks = c(0, 1, 10, 100, 1000, 10000),
labels = scales::comma,
guide = guide_colorbar(barwidth = 20, barheight = 0.5, title.position = "top", title.hjust = 0.5)
) +
coord_fixed(1.3) +
theme_minimal() +
labs(title = NULL,
x = "Longitude", y = "Latitude") +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = ggplot2::margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = ggplot2::margin(b = 10))
)
Identifying grid cells with only AIS, only SAR detections or both data types.
# Merge the datasets
combined_data <- full_join(
AIS_data_std,
SAR_data_std,
by = c("lon_std", "lat_std")
)
# Categorize each cell
combined_data <- combined_data %>%
mutate(category = case_when(
total_fishing_hours > 0 & total_presence_score > 0 ~ "Both AIS and SAR",
total_fishing_hours > 0 & (is.na(total_presence_score) | total_presence_score == 0) ~ "Only AIS",
(is.na(total_fishing_hours) | total_fishing_hours == 0) & total_presence_score > 0 ~ "Only SAR",
TRUE ~ "No fishing detected"
))
# Create the world map
world_map <- map_data("world")
# Create the plot
world_plot <- ggplot() +
geom_map(data = world_map, map = world_map,
aes(long = long, lat = lat, map_id = region),
color = "black", fill = "lightgray", size = 0.1) +
geom_tile(data = combined_data,
aes(x = lon_std, y = lat_std, fill = category)) +
scale_fill_manual(
values = c("Both AIS and SAR" = "purple",
"Only AIS" = "blue",
"Only SAR" = "red",
"No fishing detected" = "white"),
name = "Fishing Data Source",
guide = guide_legend(title.position = "top", title.hjust = 0.5)
) +
coord_fixed(1.3) +
theme_minimal() +
labs(title = "Global Fishing Detection",
subtitle = "Comparison of AIS (2017-2020) and SAR (2017-2020) data at 0.1-degree resolution",
x = "Longitude", y = "Latitude") +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = ggplot2::margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = ggplot2::margin(b = 10))
)
# Display the plot
print(world_plot)
# Get summary statistics
summary_stats <- combined_data %>%
count(category) %>%
mutate(percentage = n / sum(n) * 100) %>%
rename(`Number of cells` = n) %>%
mutate(percentage = round(percentage, 2))
# Create and display the table
kable(summary_stats,
format = "html",
col.names = c("Category", "Number of cells", "Percentage (%)"),
caption = "Summary Statistics of Data Categories") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = FALSE,
position = "center") %>%
column_spec(2, width = "150px") %>%
column_spec(3, width = "150px")| Category | Number of cells | Percentage (%) |
|---|---|---|
| Both AIS and SAR | 163095 | 9.12 |
| Only AIS | 1566190 | 87.60 |
| Only SAR | 58668 | 3.28 |
Predicting fishing hours in areas with only SAR data at a 0.1 degree resolution. Using a random forest model of >160,000 associations between SAR vessel detections and AIS fishing hours globally, geographical coordinates, distance to ports, distance to shore and bathymetry.
# Open the saved adjusted rasters
Ports_adjusted <- raster(here::here("Data", "Environmental data layers", "distance-from-port-0.1deg-adjusted.tif"))
Shore_adjusted <- raster(here::here("Data", "Environmental data layers", "distance-from-shore-0.1deg-adjusted.tif"))
Bathy_adjusted <- raster(here::here("Data", "Environmental data layers", "bathymetry-0.1deg-adjusted.tif"))
# Stack the resampled rasters
raster_stack <- stack(Shore_adjusted, Ports_adjusted, Bathy_adjusted)
# Convert the stack to a dataframe
raster_df <- as.data.frame(raster_stack, xy = TRUE)
# Rename the columns
names(raster_df) <- c("x", "y", "dist_shore", "dist_ports", "bathy")
# Remove NA values if desired
raster_df <- na.omit(raster_df)
# Convert to data.table for efficiency
setDT(raster_df)
# Round x and y to 1 decimal place for consistency
raster_df[, `:=`(
lon_std = round(x, digits = 1),
lat_std = round(y, digits = 1)
)]
# Now proceed with the join and model training
load(here::here("data","combined_data_O1deg.Rdata"))
setDT(combined_data_01deg)
# Perform the join using data.table
combined_data_with_rasters <- raster_df[combined_data_01deg,
on = .(lon_std, lat_std),
nomatch = 0]
# Convert back to dataframe if needed
combined_data_with_rasters <- as.data.frame(combined_data_with_rasters)
# Prepare the training data
training_data <- combined_data_with_rasters %>%
filter(has_AIS & has_SAR) %>%
dplyr::select(total_fishing_hours, total_presence_score, lon_std, lat_std, dist_shore, dist_ports, bathy) %>%
na.omit()
# Train the random forest model with timing
set.seed(123) # for reproducibility
#rf_timing <- system.time({
# rf_model_env <- randomForest(
# total_fishing_hours ~ total_presence_score + lon_std + lat_std + dist_shore + dist_ports + bathy,
# data = training_data,
# ntree = 500,
# importance = TRUE
# )
#})
#cat("Random Forest model training time:", rf_timing["elapsed"], "seconds\n")
# Save the new model
#save(rf_model_env, file = "rf_model_01deg_with_rasters.Rdata")
load(here::here("rf_model_01deg_with_rasters.Rdata"))
# Prepare the prediction data
prediction_data <- combined_data_with_rasters %>%
dplyr::select(total_presence_score, lon_std, lat_std, dist_shore, dist_ports, bathy)
# Make predictions
predictions <- predict(rf_model_env, newdata = prediction_data)
# Add predictions to the original dataset
combined_data_01deg <- combined_data_01deg %>%
mutate(
predicted_fishing_hours = case_when(
has_AIS ~ total_fishing_hours,
has_SAR ~ predictions[match(paste(lon_std, lat_std), paste(prediction_data$lon_std, prediction_data$lat_std))],
TRUE ~ 0
)
)
# Create the world map
world_map <- map_data("world")
# Map of predicted fishing hours only
# Prepare the data for the map
map_data <- combined_data_01deg %>%
filter(!has_AIS & has_SAR) %>%
dplyr::select(lon_std, lat_std, predicted_fishing_hours)
# Function to create map for a specific region
create_region_map <- function(data, world_map, lon_range, lat_range, title) {
ggplot() +
geom_map(data = world_map, map = world_map,
aes(long, lat, map_id = region),
color = "darkgray", fill = "lightgray", size = 0.1) +
geom_tile(data = data,
aes(x = lon_std, y = lat_std, fill = predicted_fishing_hours)) +
scale_fill_viridis(
option = "inferno",
direction = -1,
trans = "log1p",
name = "Predicted fishing hours (2017-2020)",
breaks = c(0, 1, 10, 100, 1000, 10000, 100000, 1000000),
labels = scales::comma,
guide = guide_colorbar(barwidth = 20, barheight = 0.5, title.position = "top", title.hjust = 0.5)
) +
coord_fixed(1.3, xlim = lon_range, ylim = lat_range) +
theme_minimal() +
labs(title = title,
subtitle = "0.1 degree resolution",
x = "Longitude", y = "Latitude") +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = ggplot2::margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = ggplot2::margin(b = 10))
)
}
# Global map
predicted_SAR_only_plot <- create_region_map(map_data, world_map, c(-180, 180), c(-90, 90), "Predicted Fishing Hours in Areas with Only SAR Detections")
# Caribbean map
caribbean_map <- create_region_map(map_data, world_map, c(-100, -50), c(0, 40), "Predicted Fishing Hours in the Caribbean")
# Northwestern Indian Ocean to Western European waters map
indian_european_map <- create_region_map(map_data, world_map, c(-20, 80), c(0, 70), "Predicted Fishing Hours from Northern Indian Ocean \nto Eastern Atlantic")
# Asia map
asia_map <- create_region_map(map_data, world_map, c(60, 180), c(-20, 60), "Predicted Fishing Hours in Asia")
# Print the maps
print(predicted_SAR_only_plot)
print(caribbean_map)
print(indian_european_map)
print(asia_map)
#Map of both original and predicted AIS fishing hours
# Visualize the results
predicted_plot <- ggplot() +
geom_map(data = world_map, map = world_map,
aes(long, lat, map_id = region),
color = "black", fill = "lightgray", size = 0.1) +
geom_tile(data = combined_data_01deg,
aes(x = lon_std, y = lat_std, fill = predicted_fishing_hours)) +
scale_fill_viridis(
option = "inferno",
direction = -1,
trans = "log1p",
name = "AIS Fishing Effort (hours; 2017-2020)",
breaks = c(0, 1, 10, 100, 1000, 10000, 100000, 1000000),
labels = scales::comma,
guide = guide_colorbar(barwidth = 20, barheight = 0.5, title.position = "top", title.hjust = 0.5)
) +
coord_fixed(1.3) +
theme_minimal() +
labs(title = "Global Fishing Hours (0.1 degree resolution)",
subtitle = "Based on AIS data and Random Forest predictions from SAR data",
x = "Longitude", y = "Latitude") +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = ggplot2::margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = ggplot2::margin(b = 10))
)
print(predicted_plot)
#Aggregate data to 0.5 degree resolution
# First, round the coordinates to the nearest 0.5 degree
combined_data_05deg <- combined_data_01deg %>%
mutate(
lon_05deg = round(lon_std * 2) / 2,
lat_05deg = round(lat_std * 2) / 2
)
# Aggregate the data
aggregated_data <- combined_data_05deg %>%
group_by(lon_05deg, lat_05deg) %>%
summarise(
predicted_fishing_hours = sum(predicted_fishing_hours, na.rm = TRUE),
.groups = 'drop'
)
# Create the map
predicted_plot_05deg <- ggplot() +
geom_map(data = world_map, map = world_map,
aes(long, lat, map_id = region),
color = "black", fill = "lightgray", size = 0.1) +
geom_tile(data = aggregated_data,
aes(x = lon_05deg, y = lat_05deg, fill = predicted_fishing_hours)) +
scale_fill_viridis(
option = "inferno",
direction = -1,
trans = "log1p",
name = "AIS Fishing Effort (hours; 2017-2020)",
breaks = c(0, 1, 10, 100, 1000, 10000, 100000, 1000000),
labels = scales::comma,
guide = guide_colorbar(barwidth = 20, barheight = 0.5, title.position = "top", title.hjust = 0.5)
) +
coord_fixed(1.3) +
theme_minimal() +
labs(title = "Global Fishing Hours (0.5 degree resolution)",
subtitle = "Based on AIS data and Random Forest predictions from SAR data",
x = "Longitude", y = "Latitude") +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = ggplot2::margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = ggplot2::margin(b = 10))
)
# Print the map
print(predicted_plot_05deg)
# Evaluate the model
evaluate_model <- function(model, data, log_target = FALSE) {
predictions <- predict(model, newdata = data)
if (log_target) {
predictions <- 10^predictions - 1
}
actual <- data$total_fishing_hours
# Basic Error Metrics
mae <- mean(abs(actual - predictions), na.rm = TRUE)
rmse <- sqrt(mean((actual - predictions)^2, na.rm = TRUE))
mape <- mean(abs((actual - predictions) / actual) * 100, na.rm = TRUE)
medae <- median(abs(actual - predictions), na.rm = TRUE)
# R-squared (matching randomForest's % Var explained)
r_squared <- model$rsq[length(model$rsq)]
# Adjusted R-squared
n <- length(actual)
p <- length(model$forest$independent.variable.names) # Number of predictors
adj_r_squared <- 1 - ((1 - r_squared) * (n - 1) / (n - p - 1))
# Residual Analysis
residuals <- actual - predictions
mean_residual <- mean(residuals, na.rm = TRUE)
sd_residual <- sd(residuals, na.rm = TRUE)
# Feature Importance (for Random Forest)
feature_importance <- importance(model)
return(list(
"Mean Absolute Error" = mae,
"Root Mean Squared Error" = rmse,
"Mean Absolute Percentage Error" = mape,
"Median Absolute Error" = medae,
"R-Squared" = r_squared,
"Adjusted R-Squared" = adj_r_squared,
"Mean of Residuals" = mean_residual,
"Standard Deviation of Residuals" = sd_residual,
"Feature Importance" = feature_importance
))
}
validation_data <- combined_data_with_rasters %>%
mutate(
data_category = case_when(
has_AIS & has_SAR ~ "Both AIS and SAR",
has_AIS & !has_SAR ~ "Only AIS",
!has_AIS & has_SAR ~ "Only SAR",
TRUE ~ "No fishing detected"
)
)
# Evaluate all models
validation_data <- validation_data %>% filter(data_category == "Both AIS and SAR")
results_rf_env <- evaluate_model(rf_model_env, validation_data)
# Create a dataframe for the table
results_table <- data.frame(
Metric = c("Mean Absolute Error", "Root Mean Squared Error", "Mean Absolute Percentage Error",
"Median Absolute Error", "R-Squared", "Adjusted R-Squared",
"Mean of Residuals", "Standard Deviation of Residuals"),
Value = round(c(results_rf_env$`Mean Absolute Error`,
results_rf_env$`Root Mean Squared Error`,
results_rf_env$`Mean Absolute Percentage Error`,
results_rf_env$`Median Absolute Error`,
results_rf_env$`R-Squared`,
results_rf_env$`Adjusted R-Squared`,
results_rf_env$`Mean of Residuals`,
results_rf_env$`Standard Deviation of Residuals`),
2) # Round to 2 decimal places
)
# Create and display the table
kable(results_table, format = "html", digits = 4, caption = "Model Evaluation Metrics") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = FALSE, position = "center")| Metric | Value |
|---|---|
| Mean Absolute Error | 174.91 |
| Root Mean Squared Error | 810.11 |
| Mean Absolute Percentage Error | 1250.65 |
| Median Absolute Error | 22.69 |
| R-Squared | 0.81 |
| Adjusted R-Squared | 0.81 |
| Mean of Residuals | -3.60 |
| Standard Deviation of Residuals | 810.11 |
# For feature importance, create a separate table
feature_importance <- as.data.frame(results_rf_env$`Feature Importance`)
feature_importance$Feature <- rownames(feature_importance)
feature_importance <- feature_importance[, c("Feature", "%IncMSE", "IncNodePurity")]
colnames(feature_importance) <- c("Feature", "%IncMSE", "IncNodePurity")
# Sort the feature importance table by %IncMSE in descending order
feature_importance <- feature_importance[order(-feature_importance$`%IncMSE`), ]
kable(feature_importance, format = "html", digits = 4, caption = "Feature Importance") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = FALSE, position = "center") %>%
column_spec(1, bold = TRUE) %>%
column_spec(2:3, width = "150px")| Feature | %IncMSE | IncNodePurity | |
|---|---|---|---|
| total_presence_score | total_presence_score | 127.1595 | 737080845352 |
| lon_std | lon_std | 80.5654 | 557956385485 |
| dist_ports | dist_ports | 55.9307 | 312138319465 |
| bathy | bathy | 48.4341 | 238192589722 |
| lat_std | lat_std | 48.0964 | 540028288659 |
| dist_shore | dist_shore | 46.3306 | 213555877780 |